Abstract: Phenotyping trials may not take into account sufficient spatial context to infer quantitative disease resistance of recommended varieties in commercial production settings. Recent ecological theory—the dispersal scaling hypothesis—provides evidence that host heterogeneity and scale of host heterogeneity interact in a predictable and straightforward manner to produce a unimodal (“humpbacked”) distribution of epidemic outcomes. This suggests that the intrinsic artificiality (scale and design) of experimental set-ups may lead to spurious conclusions regarding the resistance of selected elite cultivars, due to the failure of experimental efforts to accurately represent disease pressure in real agricultural situations. In this model-based study we investigate the interaction of host heterogeneity and scale as a confounding factor in the inference from ex-situ assessment of quantitative disease resistance to commercial production settings. We use standard modelling approaches in plant disease epidemiology and a number of different agronomic scenarios. Model results revealed that the interaction of heterogeneity and scale is a determinant of relative varietal performance under epidemic conditions. This is a previously unreported phenomenon that could provide a new basis for informing the design of future phenotyping platforms, and optimising the scale at which quantitative disease resistance is assessed.

Abstract: Developing cultivars with improved adaptation to drought and heat stressed environments is a priority for plant breeders. Canopy temperature (CT) is a useful tool for phenotypic selection of tolerant genotypes, as it integrates many physiological responses into a single low-cost measurement. The objective of this study was to determine the ability of CT to predict grain yield within the flow of a wheat breeding program and assess its utility as a tool for indirect selection. CT was measured in both heat and drought stressed field experiments in northwest Mexico on 18 breeding trials totaling 504 spring wheat lines from the International Maize and Wheat Improvement Center (CIMMYT) Irrigated Bread Wheat program. In the heat treatment, CT was significantly correlated with yield (r = −0.26) across all trials, with a maximum coefficient of determination within the individual trials of R2 = 0.36. In the drought treatment, a significant correlation across all trials was only observed when days to heading or plant height was used as a covariate. However, the coefficient of determination within individual trials had a maximum of R2 = 0.54, indicating that genetic background may impact the ability of CT to predict yield. Overall a negative slope in the heat treatment indicated that a cooler canopy provided a yield benefit under stress, and implementing selection strategies for CT may have potential for breeding tolerant genotypes.

Abstract: Turfgrass growth, performance and quality are affected by abiotic stress factors and are of primary concern for persons managing turfgrass areas under seasonal tropical climates. Under these conditions, common Savannahgrass (SG) may have a performance advantage over imported hybrid turfgrasses. A greenhouse study was conducted to comparatively evaluate the performance of tropical turfgrasses exposed to water and compaction related stresses across a range of soils, with or without the addition of a surface sand layer. Turfgrass productivity and quality was monitored over a four-month growth period. Clipping yield (CY) was lower at the higher compaction effort for all turfgrasses, but across all stresses, drought (D) and waterlogging (WL) resulted in lower CY. Values were significantly lower under D. SG had the highest clipping yield across all soils. The chlorophyll index (CI) was lower for all turfgrasses under water-induced stress compared to compaction stresses. SG had a significantly higher CI across all stress treatments. Correlation analysis showed a positive (r² = 0.420) and significant (p < 0.05) relationship between CY and CI. Similar to CI, stress type influenced turfgrass visual quality (VQ), with D stress, resulting in the lowest VQ rating among turfgrasses. Bermudagrass (BG) had the lowest VQ across all stress treatments, whilst, comparatively, Zoysiagrass (ZG) had significantly higher VQ under high compaction (HC), low compaction (LC) and WL stress. Overall, SG showed a higher level of tolerance to applied stresses and warrants greater attention as a potential turfgrass under tropical conditions.

Abstract: Traditional rice varieties maintained and cultivated by farmers are likely sources of germplasm for breeding new rice varieties. They possess traits potentially adaptable to a wide range of abiotic and biotic stresses. Characterization of these germplasms is essential in rice breeding and provides valued information on developing new rice cultivars. In this study, 307 traditional rice varieties newly conserved at the PhilRice genebank were characterized to assess their phenotypic diversity using 57 morphological traits. Using the standardized Shannon-Weaver diversity index, phenotypic diversity indices averaged at 0.73 and 0.45 for quantitative and qualitative traits, respectively. Correlation analyses among agro-morphological traits showed a high positive correlation in some traits such as culm number and panicle number, flag leaf width and leaf blade width, grain width and caryopsis width. Cluster analysis separated the different varieties into various groups. Principal component analysis (PCA) showed that seven independent principal components accounted for 74.95% of the total variation. Component loadings for each principal component showed morphological characters, such as culm number, panicle number and caryopsis ratio that were among the phenotypic traits contributing positive projections in three principal components that explained 48% of variation. Analyses of results showed high diversity in major traits assessed in farmers’ rice varieties. Based on plant height and maturity, 11 accessions could be potential donor parents in a rice breeding program. Future collection trips and characterization studies would further enrich diversity, in particular traits low in diversity, such as anthocyanin coloration, awn presence, awn color, culm habit, panicle type and panicle branching.

Abstract: Plant breeding trials are extensive (100s to 1000s of plots) and are difficult and expensive to monitor by conventional means, especially where measurements are time-sensitive. For example, in a land-based measure of canopy temperature (hand-held infrared thermometer at two to 10 plots per minute), the atmospheric conditions may change greatly during the time of measurement. Such sensors measure small spot samples (2 to 50 cm2), whereas image-based methods allow the sampling of entire plots (2 to 30 m2). A higher aerial position allows the rapid measurement of large numbers of plots if the altitude is low (10 to 40 m) and the flight control is sufficiently precise to collect high-resolution images. This paper outlines the implementation of a customized robotic helicopter (gas-powered, 1.78-m rotor diameter) with autonomous flight control and software to plan flights over experiments that were 0.5 to 3 ha in area and, then, to extract, straighten and characterize multiple experimental field plots from images taken by three cameras. With a capacity to carry 1.5 kg for 30 min or 1.1 kg for 60 min, the system successfully completed >150 flights for a total duration of 40 h. Example applications presented here are estimations of the variation in: ground cover in sorghum (early season); canopy temperature in sugarcane (mid-season); and three-dimensional measures of crop lodging in wheat (late season). Together with this hardware platform, improved software to automate the production of ortho-mosaics and digital elevation models and to extract plot data would further benefit the development of high-throughput field-based phenotyping systems.

Abstract: Unsustainable agronomic practices and environmental change necessitate a revolution in agricultural production to ensure food security. A new generation of crops that yield more with fewer inputs and are adapted to more variable environments is needed. However, major changes in breeding programmes may be required to achieve this goal. By using the genetic variation in crop yield in specific target environments that vary in soil type, soil management, nutrient inputs and environmental stresses, robust traits suited to specific conditions can be identified. It is here that long-term experimental platforms and field phenotyping have an important role to play. In this review, we will provide information about some of the field-based platforms available and the cutting edge phenotyping systems at our disposal. We will also identify gaps in our field phenotyping resources that should be filled. We will go on to review the challenges in producing crop ideotypes for the dominant management systems for which we need sustainable solutions, and we discuss the potential impact of three-way interactions between genetics, environment and management. Finally, we will discuss the role that modelling can play in allowing us to fast-track some of these processes to allow us to make rapid gains in agricultural sustainability.

Abstract: Microbial infections of crop plants present an ongoing threat to agricultural production. However, in recent years, we have developed a more nuanced understanding of the ecological role of microbes and how they interact with plants. This includes an appreciation of the influence of crop physiology and environmental conditions on the expression of disease symptoms, the importance of non-pathogenic microbes on host plants and pathogens, and the capacity for plants to act as hosts for human pathogens. Alongside this we now have a variety of tools available for the identification and quantification of microbial infections on crops grown under field conditions. This review summarises some of the consequences of microbial infections in crop plants, and discusses how new and established assessment tools can be used to understand these processes. It challenges our current assumptions in yield loss relationships and offers understanding of the potential for more resilient crops.